Wireless Personal Communications

, Volume 95, Issue 3, pp 2049–2072 | Cite as

The Real-Time Detection and Prediction Method for Ballistic Aircraft Based on Distributed Sensor Networks

  • Lejiang GuoEmail author
  • Hao Li
  • Fangxin Chen
  • YaHui Hu


Space-based detection satellite and remote detection phased array radar are important parts of space security detection system. The aircraft’s detection, tracking and parameter estimation, trajectory prediction with detection satellite and radar are primary problems to be solved in the early detection of ballistic aircraft. This paper mainly studies the simulation of aircraft’s trajectory in early detection phase, the establishment of a dynamic model of the active segment and trajectory simulation and the simulation to generate a multi-level trajectory vehicle’s trajectory data based on the estimation of the key parameters of the aircraft in the single star observing conditions, the trajectory forecast and radar observation conditions for aircraft tracking. Due to the incompleteness of measurements for the single-satellite detection and the bad convergence, this paper proposes a fired at the focal plane method based on the priori template, establish a complete formula derivation algorithm processes, establish a priori standard ballistic template with the simulation trajectory data and the validity of the method using Monte Carlo simulation. Based on the MATLAB graphical user interface, it builds a simulation platform of ballistics aircraft detection probe which can effectively complete the early detection of scene simulation and demonstration. The simulation results show that the method can solve the bad convergence problems of the detection of a single star and it suits for the application to the ballistic vehicle’s key point estimation.


Ballistic simulation Target tracking Parameter estimation Interactive multiple models 



This work was supported by the Chinese National Natural Science Foundation (No. 60773190, 60802002).

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Early Warning Surveillance IntelligenceAir Force Early Warning AcademyWuhanChina
  2. 2.Equipment Development and Application Research CenterAir Force Engineering UniversityXi’anChina

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